Intelligent Workspace Acquisition, Comprehension and Exploitation for Mobile Autonomy in Infrastructure Denied Environments

智能工作空间的获取、理解和利用，实现基础设施匮乏环境中的移动自主

• 批准号: EP/J012017/1
• 负责人: Paul Newman
• 金额: $ 139.35万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ012017%2F1
• 关键词: Intelligent; Workspace; Acquisition; Comprehension; Exploitation

Vehicles will only get smarter. There will always be a desire for more machine intelligence and autonomy. Our needs and expectations are ever increasing. As a result, we continue to pack more sensors and more computation into the robots that carry, transport, labour for and defend us. Here we interpret autonomy as a robot's ability to sense, understand and ultimately act of its own accord in its operating environment. This proposal is about giving autonomous vehicles the ability to navigate in difficult conditions over long periods of time. Conditions become "difficult" when GPS is denied or only intermittently available, when little, if anything, is known about the environment, when communications are sporadic and unreliable or when operating conditions like lighting change unpredictably. And yet, somewhat perversely, it is often in just these conditions that our need to navigate is greatest: consider, for example, the surveying of buildings in a stricken nuclear facility such as Fukushima, or the autonomous driving of cars at night in cities where GPS coverage is poor. Intelligent navigation lies at the heart of much of mobile robotics research. It finds application in remote inspection, autonomous urban driving, defence, logistics, security and space robotics.We shall consider how machines can acquire and manage the information they need to operate persistently in workspaces of our choosing. The goal is to demonstrate that performance improves through use and over time - something that comes naturally to humans and is immensely valuable in a machine. This goal poses questions about how the computers that control robots should represent their environment in a plastic fashion - one which can be stretched and pulled into different shapes over time. We also need to consider how to enable machines to decide how to act to improve their understanding of the world - alone and in concert with other vehicles, each with different sensors and capabilities. How can vehicle sensors be calibrated transparently and continuously? How can motion be planned to maximise both the coverage of inspections and the accuracy of workspace assessments? How can successful operation be guaranteed in the presence of unreliable or short-range communications?This interweaving of the state of the art in navigation, planning and communications management is unusual and will allow us to ask and provide answers to challenging robotics science questions which, when exploited, will have a dramatic impact on the robots that will become indispensable in the future.

汽车只会变得更聪明。人们总是渴望更多的机器智能和自主性。我们的需求和期望越来越高。因此，我们继续将更多的传感器和更多的计算能力装入机器人中，这些机器人为我们搬运、运输、劳动和保卫我们。在这里，我们将自主性解释为机器人在其操作环境中感知、理解并最终自行雅阁行动的能力。该提案旨在让自动驾驶汽车能够在困难的条件下长时间航行。当全球定位系统被拒绝或仅间歇性可用时，当对环境知之甚少时，当通信时断时续且不可靠时，或当照明等操作条件不可预测地变化时，条件变得“困难”。然而，有些令人困惑的是，正是在这些条件下，我们对导航的需求最大：例如，在福岛等受灾核设施中测量建筑物，或者在GPS覆盖较差的城市中夜间自动驾驶汽车。智能导航是许多移动的机器人研究的核心。它可以应用于远程检测、自动城市驾驶、国防、物流、安全和空间机器人。我们将考虑机器如何获取和管理它们在我们选择的环境中持续运行所需的信息。我们的目标是证明性能会通过使用和随着时间的推移而提高--这对人类来说是自然而然的，而对机器来说则是非常有价值的。这一目标提出了一个问题，即控制机器人的计算机应该如何以一种塑料的方式来代表他们的环境-一种可以随着时间的推移被拉伸和拉成不同形状的方式。我们还需要考虑如何让机器决定如何行动，以提高它们对世界的理解--单独行动，以及与其他车辆协同行动，每种车辆都有不同的传感器和能力。如何透明、连续地校准车辆传感器？如何规划运动，以最大限度地扩大视察范围和工作空间评估的准确性？在通信不可靠或距离短的情况下，如何保证成功运作？这种在导航、规划和通信管理方面的最新技术的交织是不寻常的，将使我们能够提出并回答具有挑战性的机器人科学问题，这些问题一旦被利用，将对未来必不可少的机器人产生巨大影响。

项目成果

Shady dealings: Robust, long-term visual localisation using illumination invariance

• DOI: 10.1109/icra.2014.6906961
• 发表时间: 2014-09
• 期刊: 2014 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: C. McManus;W. Churchill;William P. Maddern;Alexander D. Stewart;P. Newman

Learning on the Job : Improving Robot Perception Through Experience

在工作中学习：通过经验提高机器人感知

• 发表时间: 2014
• 作者: Corina Gurau;Jeffrey Hawke;Chi Hay Tong;I. Posner
• 通讯作者: I. Posner

Underground Incrementally Deployed Magneto-Inductive 3-D Positioning Network

• DOI: 10.1109/tgrs.2016.2540722
• 发表时间: 2016-08-01
• 期刊: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
• 影响因子: 8.2
• 作者: Abrudan, Traian E.;Xiao, Zhuoling;Trigoni, Niki
• 通讯作者: Trigoni, Niki

Can Priors Be Trusted? Learning to Anticipate Roadworks

先验者可以信任吗？

• 发表时间: 2012
• 作者: B. Mathibela

Lighting invariant urban street classification

• DOI: 10.1109/icra.2014.6907082
• 发表时间: 2014-05
• 期刊: 2014 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: B. Upcroft;C. McManus;W. Churchill;William P. Maddern;P. Newman